68 research outputs found
Interferometry-based modal analysis with finite aperture effects
We analyze the effects of aperture finiteness on interferograms recorded to
unveil the modal content of optical beams in arbitrary basis using generalized
interferometry. We develop a scheme for modal reconstruction from
interferometric measurements that accounts for the ensuing clipping effects.
Clipping-cognizant reconstruction is shown to yield significant performance
gains over traditional schemes that overlook such effects that do arise in
practice. Our work can inspire further research on reconstruction schemes and
algorithms that account for practical hardware limitations in a variety of
contexts
Sensor Scheduling for Energy-Efficient Target Tracking in Sensor Networks
In this paper we study the problem of tracking an object moving randomly
through a network of wireless sensors. Our objective is to devise strategies
for scheduling the sensors to optimize the tradeoff between tracking
performance and energy consumption. We cast the scheduling problem as a
Partially Observable Markov Decision Process (POMDP), where the control actions
correspond to the set of sensors to activate at each time step. Using a
bottom-up approach, we consider different sensing, motion and cost models with
increasing levels of difficulty. At the first level, the sensing regions of the
different sensors do not overlap and the target is only observed within the
sensing range of an active sensor. Then, we consider sensors with overlapping
sensing range such that the tracking error, and hence the actions of the
different sensors, are tightly coupled. Finally, we consider scenarios wherein
the target locations and sensors' observations assume values on continuous
spaces. Exact solutions are generally intractable even for the simplest models
due to the dimensionality of the information and action spaces. Hence, we
devise approximate solution techniques, and in some cases derive lower bounds
on the optimal tradeoff curves. The generated scheduling policies, albeit
suboptimal, often provide close-to-optimal energy-tracking tradeoffs
Sensor Management for Tracking in Sensor Networks
We study the problem of tracking an object moving through a network of
wireless sensors. In order to conserve energy, the sensors may be put into a
sleep mode with a timer that determines their sleep duration. It is assumed
that an asleep sensor cannot be communicated with or woken up, and hence the
sleep duration needs to be determined at the time the sensor goes to sleep
based on all the information available to the sensor. Having sleeping sensors
in the network could result in degraded tracking performance, therefore, there
is a tradeoff between energy usage and tracking performance. We design sleeping
policies that attempt to optimize this tradeoff and characterize their
performance. As an extension to our previous work in this area [1], we consider
generalized models for object movement, object sensing, and tracking cost. For
discrete state spaces and continuous Gaussian observations, we derive a lower
bound on the optimal energy-tracking tradeoff. It is shown that in the low
tracking error regime, the generated policies approach the derived lower bound
On the inverse problem of source reconstruction from coherence measurements
We consider an inverse source problem for partially coherent light
propagating in the Fresnel regime. The data is the coherence of the field
measured away from the source. The reconstruction is based on a minimum residue
formulation, which uses the authors' recent closed-form approximation formula
for the coherence of the propagated field. The developed algorithms require a
small data sample for convergence and yield stable inversion by exploiting
information in the coherence as opposed to intensity-only measurements.
Examples with both simulated and experimental data demonstrate the ability of
the proposed approach to simultaneously recover complex sources in different
planes transverse to the direction of propagation
Spatial coherence of fields from generalized sources in the Fresnel regime
Analytic expressions of the spatial coherence of partially coherent fields
propagating in the Fresnel regime in all but the simplest of scenarios are
largely lacking and calculation of the Fresnel transform typically entails
tedious numerical integration. Here, we provide a closed-form approximation
formula for the case of a generalized source obtained by modulating the field
produced by a Gauss-Shell source model with a piecewise constant transmission
function, which may be used to model the field's interaction with objects and
apertures. The formula characterizes the coherence function in terms of the
coherence of the Gauss-Schell beam propagated in free space and a
multiplicative term capturing the interaction with the transmission function.
This approximation holds in the regime where the intensity width of the beam is
much larger than the coherence width under mild assumptions on the modulating
transmission function. The formula derived for generalized sources lays the
foundation for the study of the inverse problem of scene reconstruction from
coherence measurements.Comment: Accepted for publication in JOSA
Multi-modal Non-line-of-sight Passive Imaging
We consider the non-line-of-sight (NLOS) imaging of an object using the light
reflected off a diffusive wall. The wall scatters incident light such that a
lens is no longer useful to form an image. Instead, we exploit the 4D spatial
coherence function to reconstruct a 2D projection of the obscured object. The
approach is completely passive in the sense that no control over the light
illuminating the object is assumed and is compatible with the partially
coherent fields ubiquitous in both the indoor and outdoor environments. We
formulate a multi-criteria convex optimization problem for reconstruction,
which fuses the reflected field's intensity and spatial coherence information
at different scales. Our formulation leverages established optics models of
light propagation and scattering and exploits the sparsity common to many
images in different bases. We also develop an algorithm based on the
alternating direction method of multipliers to efficiently solve the convex
program proposed. A means for analyzing the null space of the measurement
matrices is provided as well as a means for weighting the contribution of
individual measurements to the reconstruction. This paper holds promise to
advance passive imaging in the challenging NLOS regimes in which the intensity
does not necessarily retain distinguishable features and provides a framework
for multi-modal information fusion for efficient scene reconstruction
Compressive optical interferometry
Compressive sensing (CS) combines data acquisition with compression coding to
reduce the number of measurements required to reconstruct a sparse signal. In
optics, this usually takes the form of projecting the field onto sequences of
random spatial patterns that are selected from an appropriate random ensemble.
We show here that CS can be exploited in `native' optics hardware without
introducing added components. Specifically, we show that random sub-Nyquist
sampling of an interferogram helps reconstruct the field modal structure. The
distribution of reduced sensing matrices corresponding to random measurements
is provably incoherent and isotropic, which helps us carry out CS successfully
BOSS: Bidirectional One-Shot Synthesis of Adversarial Examples
The design of additive imperceptible perturbations to the inputs of deep
classifiers to maximize their misclassification rates is a central focus of
adversarial machine learning. An alternative approach is to synthesize
adversarial examples from scratch using GAN-like structures, albeit with the
use of large amounts of training data. By contrast, this paper considers
one-shot synthesis of adversarial examples; the inputs are synthesized from
scratch to induce arbitrary soft predictions at the output of pre-trained
models, while simultaneously maintaining high similarity to specified inputs.
To this end, we present a problem that encodes objectives on the distance
between the desired and output distributions of the trained model and the
similarity between such inputs and the synthesized examples. We prove that the
formulated problem is NP-complete. Then, we advance a generative approach to
the solution in which the adversarial examples are obtained as the output of a
generative network whose parameters are iteratively updated by optimizing
surrogate loss functions for the dual-objective. We demonstrate the generality
and versatility of the framework and approach proposed through applications to
the design of targeted adversarial attacks, generation of decision boundary
samples, and synthesis of low confidence classification inputs. The approach is
further extended to an ensemble of models with different soft output
specifications. The experimental results verify that the targeted and
confidence reduction attack methods developed perform on par with
state-of-the-art algorithms
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